Mid-Level Software Engineer, Full Stack and Generative AI

Nexiva•Rockville, MD

2d•$55•Hybrid

About The Position

This role involves designing and developing scalable full-stack applications with Angular frontends and microservices-based backends. The engineer will build secure, high-performance RESTful and GraphQL APIs using modern backend frameworks like Java/Spring Boot and Python/FastAPI. They will also develop responsive and accessible user interfaces using Angular and TypeScript, and collaborate with cross-functional teams to translate business requirements into technical solutions. A key aspect of the role is the responsible utilization of AI-assisted development tools while maintaining code quality and security. The position also includes implementing Generative AI solutions such as LLM integrations, prompt engineering, and Retrieval-Augmented Generation (RAG) pipelines, and partnering with data science and engineering teams for ML model integration and serving infrastructure. Promoting responsible AI practices is also a focus. Additionally, the role requires designing and maintaining CI/CD pipelines, implementing infrastructure-as-code and containerized deployments, and integrating automated testing, security scanning, and quality assurance. The engineer will also provide technical leadership, mentorship, and guidance to junior engineers, contribute to architecture discussions, and ensure systems align with enterprise security, compliance, audit, and governance standards.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or related field.
5–7 years of professional software engineering experience.
Strong proficiency in Python and/or Java backend development.
3+ years of hands-on experience with Angular, TypeScript, RxJS, and state management frameworks such as NgRx.
Experience designing and implementing RESTful APIs and/or GraphQL services.
Hands-on experience with AWS services including Lambda, ECS/EKS, API Gateway, S3, RDS, and DynamoDB.
Experience with containerization technologies such as Docker and Kubernetes.
Proficiency with relational and NoSQL databases including PostgreSQL, MongoDB, and DynamoDB.
Strong understanding of application security principles including OWASP Top 10, secrets management, and least-privilege access.
At least 1 year of experience working with LLMs, prompt engineering, embedding models, or platforms such as OpenAI, Anthropic, or AWS Bedrock.

Nice To Haves

Experience within regulated or enterprise environments.
Experience building human-in-the-loop review systems, annotation platforms, or approval workflows for AI-generated outputs.
Familiarity with LangChain, LlamaIndex, or similar LLM orchestration frameworks.
Experience implementing RAG architectures using vector databases such as Pinecone, Weaviate, pgvector, or OpenSearch.
Familiarity with event-driven architectures and messaging systems including Kafka, AWS SQS/SNS, or Kinesis.
Exposure to observability and monitoring tools such as Datadog, Grafana, or CloudWatch.
Experience with microservices patterns including circuit breakers, service mesh, and distributed tracing.
Familiarity with feature flagging, canary deployments, and progressive delivery strategies.
Contributions to open-source projects or technical publications related to AI/ML.

Responsibilities

Design and develop scalable full stack applications with Angular frontends and microservices-based backends.
Build secure, high-performance RESTful and GraphQL APIs using modern backend frameworks such as Java/Spring Boot and Python/FastAPI.
Develop responsive and accessible user interfaces using Angular and TypeScript.
Collaborate with cross-functional teams including data engineering, security, and business stakeholders to translate business requirements into technical solutions.
Utilize AI-assisted development tools responsibly while maintaining code quality and security best practices.
Implement Generative AI solutions including LLM integrations, prompt engineering, and Retrieval-Augmented Generation (RAG) pipelines.
Partner with data science and engineering teams to integrate ML models and support model serving infrastructure.
Promote responsible AI practices including human oversight, validation, and bias monitoring.
Design and maintain CI/CD pipelines using tools such as Jenkins.
Implement infrastructure-as-code and containerized deployments using Docker and Kubernetes.
Integrate automated testing, security scanning, and quality assurance into deployment pipelines.
Provide mentorship and technical guidance to junior engineers through code reviews and collaborative development.
Contribute to architecture discussions, engineering standards, and best practices.
Ensure systems align with enterprise security, compliance, audit, and governance standards.